Prediction of Covid-19 and post Covid-19 patients with reduced feature extraction using Machine Learning Techniques

Bano, Shehr; Hussain, Syed Fawad

doi:10.1109/fit53504.2021.00017

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…During the COVID-19 outbreak, many new developments related to healthcare utilized electromyography (EMG) or electroencephalogram (EEG). Setiawan et al proposed a stroke rehabilitation monitoring method using EEG [4], Bano and Hussain could identify patients that were infected by COVID-19 using the EMG technique [5], and L. Jiang et al developed an advising device for at-home exercise using sEMG [6]. Some disabled and paralyzed patients, however, who have both arms amputated cannot wear face masks independently.…”

Section: Introductionmentioning

confidence: 99%

Portable Face‐Shielding Device Based on sEMG Considering the COVID‐19 Scenario

Lyu

Liu

Shao

et al. 2023

Journal of Healthcare Engineering

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Wearing a mask greatly reduced the possibility of infection during the COVID-19 pandemic. However, major inconveniences occur regarding patients with upper limb amputations, as they cannot independently wear masks. As a result, bacterial contamination is caused by medical staff touching the quilt when helping. Furthermore, this effect can occur with ordinary people due to accidental touch. This research aims to design an automatic and portable face shield assistive device based on surface electromyography (sEMG) signals. A concise face shield-wearing mechanism was built through 3D printing. A novel decision-making control method regarding a feature extraction model of 16 signal features and a Softmax classification neural network model were developed and tested on an STM32 microcontroller unit (MCU). The optimized electrode was fabricated using a carbon nanotube (CNT)/polydimethylsiloxane (PDMS). The design was further integrated and tested, showing a promising future for further implementation.

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Section: Introductionmentioning

confidence: 99%